The present disclosure relates to an omni-channel marketing curation system based on big data, and more particularly, to an omni-channel marketing curation system based on big data capable of closely viewing a sales status for each store of a company in real time through animation and accurately measuring a marketing effect for each customer group by organically combining to manage online and offline stores in an omni point of view.
Global distribution companies are strengthening their competitive power while concentrating on provision of a differentiated service experience as well as just sales of products. Marketers in the companies consider very importantly whether their marketing strategies are efficient and how customers react to products released according to their marketing strategies, and want to accurately predict marketing effects.
According to a typical method, in order to know customers' reactions to products sold in an online store or offline stores, after sales data for each store is collected and the collected sales data is reflected in a statistical system, an analysis program is executed. Alternatively, a process is required to be undergone in which a developer changes and corrects a source code of an analysis program appropriately, and executes the analysis program. According to such a method, a lot of time is taken for data collection and it is difficult to compile statistics for live field sales data due to the time taken to collect the data. In addition, after application to a system, it is difficult to check a graph presented through the analysis program.
In addition, even though a customer purchased a corresponding product through advertisement or sales promotion, it may not be known whether the customer will visit again a corresponding store and there is not a method for predicting a future behavior of the customer since the effortful marketing becomes finished as a one-time event.
In a case where an online store and offline stores are run concurrently, it is difficult to practically analyze showrooming in which products are compared in an offline store and then purchase is performed at a lowest price in an online store and reverse showrooming in which products are compared in an online store and then purchase is performed at a lowest price in an offline store.
Furthermore, in order to construct and operate a customer relationship management (CRM) system for collecting and analyzing marketing data, a lot of money is necessary for extracting data from a legacy system, and for refining, data-warehousing (DW), and analyzing the extracted data. A use effect thereof is also uncertain.
The present disclosure dramatically improves a typical marketing scheme to present a method for allowing a user to easily know an ever-changing customer's reaction and field data.